Myocardial Infarction

Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization.

Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rh...

Study on Horizon Scanning with a Focus on the Development of AI-Based Medical Products: Citation Network Analysis.

Horizon scanning for innovative technologies that might be applied to medical products and requires new assessment approaches to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. The purpose of this study is to confirm that citation network analysis and text mining for bibliographic information analysis can be used for horizon scanning of t...

Intelligent Monitoring of Care Status for COPD Patients Based on Deep Learning.

To discuss the application method and effect of COPD patients in deep learning in intelligent monitoring, two groups were used under a reasonable sele...

Evolution of single-lead ECG for STEMI detection using a deep learning approach.

BACKGROUND: While ST-Elevation Myocardial Infarction (STEMI) door-to-balloon times are often below 90 min, symptom to door times remain long at 2.5-h,...

Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study.

Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability (HRV) levels compared to healthy controls. This research aim...

Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding.

Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases...

Study on the use of standard 12-lead ECG data for rhythm-type ECG classification problems.

BACKGROUND AND OBJECTIVES: Most deep-learning-related methodologies for electrocardiogram (ECG) classification are focused on finding an optimal deep-...

DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine.

Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem fo...

Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.

Artificial Intelligence and machine learning (ML) methods are promising for risk-stratification, but the added benefit over traditional statistical me...

Classification of electrocardiogram signals with waveform morphological analysis and support vector machines.

Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the auto...

Rational and design of ST-segment elevation not associated with acute cardiac necrosis (LESTONNAC). A prospective registry for validation of a deep learning system assisted by artificial intelligence.

BACKGROUND: Patients with chest pain and persistent ST segment elevation (STE) may not have acute coronary occlusions or serum troponin curves suggest...

Review of Deep Learning-Based Atrial Fibrillation Detection Studies.

Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiogr...

Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation.

Recently, deep learning approaches for MR motion artifact correction have been extensively studied. Although these approaches have shown high performa...

A Novel Prediction Model of Acute Kidney Injury Based on Combined Blood Variables in STEMI.

BACKGROUND: Development of acute kidney injury (AKI) is associated with poor prognosis in patients with ST-segment elevation myocardial infarction (ST...

Robustness of convolutional neural networks to physiological electrocardiogram noise.

The electrocardiogram (ECG) is a widespread diagnostic tool in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning method...

Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification.

Deep learning (DL) has achieved promising performance in detecting common abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic ...

Intentional Observational Clinical Research Design: Innovative Design for Complex Clinical Research Using Advanced Technology.

The growing use of robots in nursing and healthcare facilities has prompted increasing research on human-robot interactions. However, specific researc...

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead ...

Classification of Arrhythmia in Heartbeat Detection Using Deep Learning.

The electrocardiogram (ECG) is one of the most widely used diagnostic instruments in medicine and healthcare. Deep learning methods have shown promise...

The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition.

BACKGROUND: Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized b...